Implementing Artificial Neural Network Training Process in Python

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 1. Data Preparation
# Load your dataset, split it into training and testing sets, and preprocess the data.

# Example dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0  # Normalize pixel values

# 2. Model Design
# Define the architecture of your neural network.

model = keras.Sequential([
    layers.Flatten(input_shape=(28, 28)),  # Input layer (Flatten 28x28 images)
    layers.Dense(128, activation='relu'),  # Hidden layer with ReLU activation
    layers.Dropout(0.2),  # Dropout layer to reduce overfitting
    layers.Dense(10, activation='softmax')  # Output layer with softmax activation (10 classes)
])

# 3. Compile the Model
# Specify the loss function, optimizer, and evaluation metrics.

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 4. Model Training
# Train the model on the training data.

model.fit(x_train, y_train, epochs=5)  # Adjust the number of epochs as needed

# 5. Model Evaluation
# Evaluate the model's performance on the test data.

test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"Test accuracy: {test_acc}")

# 6. Model Prediction
# Use the trained model to make predictions.

predictions = model.predict(x_test)

# 7. Fine-Tuning (Optional)
# You can fine-tune hyperparameters, model architecture, and other aspects to improve performance.

# Example: Adjust the learning rate of the optimizer
custom_optimizer = keras.optimizers.Adam(learning_rate=0.001)
model.compile(optimizer=custom_optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Continue training if needed
model.fit(x_train, y_train, epochs=5)

# Save the trained model if desired
model.save("my_model.h5")